Production AI Scoring: Processing 10,000+ Job Listings Daily

Building an AI scoring pipeline to process over 10,000 job listings daily reveals critical lessons for engineers. Initial challenges included latency, cost, and inconsistency, which were addressed through structured output and function calling methods.

Ultimately, a four-stage architecture was developed: ingestion, pre-filtering, scoring, and delivery. These processes improved efficiency while reducing API costs. Additionally, cost control strategies significantly lowered overall expenses. These experiences provide valuable insights into making AI-driven systems more reliable from an engineering perspective.